Atrial Fibrillation Recurrence Prediction with Right Atrial CT Features

297 patients who underwent catheter ablation for atrial fibrillation, with data collected by Xiaonan Han. The dataset likely contains 37 clinical and cardiac computed tomography imaging features used to develop machine learning models for predicting recurrence in paroxysmal and persistent AF types.

Use Cases

• Predicting recurrence of paroxysmal atrial fibrillation based on right atrial appendage short diameter.
• Predicting recurrence of persistent atrial fibrillation based on right atrial volume.
• Developing explainable clinical decision support tools using SHAP analysis.
• Investigating nonlinear relationships between atrial structural parameters and ablation outcomes.

Strengths

• Includes 297 patient records, split into 230 paroxysmal AF and 67 persistent AF cases.
• Models achieved AUC scores of 0.831 for paroxysmal AF and 0.917 for persistent AF.
• License is CC-BY-4.0, permitting open reuse.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Data may reflect temporal and source bias inherent to the original study.

Provenance

Source

figshare

Collection Method

Retrospective enrollment of patients undergoing radiofrequency catheter ablation.

Freshness

Last updated 2026-04-29 05:57:16; freshness should be verified.